AI Agent Memory Architecture with Spring AI

Spring AI智能体分层记忆架构实战

掌握用Java、Spring AI与PostgreSQL构建AI智能体分层记忆系统，涵盖角色、情景、语义及工作记忆，实现向量检索与异步记忆管道，打造准确记住用户的智能后端。

Published 5/2026
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 1h 48m | Size: 1.05 GB

Build layered memory systems with Java, Spring AI, PostgreSQL, pgvector, and scalable backend architecture

What you’ll learn
Design AI agents with layered memory using Spring AI Advisors and PostgreSQL
Build persona, episodic, semantic, and working memory for AI assistants
Implement vector-based memory retrieval using pgvector and embeddings
Create AI systems that remember users correctly across conversations
Build scalable async memory pipelines for production-style AI backends
Develop backend AI applications that learn user preferences over time
Understand why chat history alone is not real memory for AI agents

Requirements
Basic knowledge of Java and Spring Boot is recommended
Prior experience with SQL databases like PostgreSQL will help
No prior AI or machine learning experience is required
Curiosity about how modern AI assistants remember users across conversations

Description
Most AI applications do not truly remember users.
They simply replay chat history.

In this course, you will learn how to design and implement real memory systems for AI agents using Java, Spring AI, PostgreSQL, and pgvector.

Using a practical AI Travel Planner project, you will build a layered memory architecture that enables AI assistants to remember users correctly across conversations.

This is a backend engineering focused course designed for developers who want to move beyond basic chat applications and build production-style AI systems.

What You’ll Build

• Working memory using conversation history

• Persona memory for persistent user facts

• Episodic memory using conversation summaries

• Semantic memory using learned preferences

• Vector similarity search with pgvector

• Async memory processing pipelines

• Centralized prompt assembly using Spring AI Advisors

What You’ll Learn

• Why chat history is not real AI memory

• How modern AI memory systems are structured

• How to design layered memory architectures

• How embeddings and vector search work in practice

• How to retrieve relevant memory dynamically

• How to build scalable AI backend pipelines

• How to personalize AI behavior across conversations

Technologies Used

• Java

• Spring Boot

• Spring AI

• PostgreSQL

• pgvector

By the end of this course, you will have a complete understanding of how real AI memory systems are designed and implemented in modern backend applications.